Machine Learning to Quantify Physical Activity in Children with Cerebral Palsy: Comparison of Group, Group-Personalized, and Fully-Personalized Activity Classification Models

Machine Learning to Quantify Physical Activity in Children with Cerebral Palsy: Comparison of Group, Group-Personalized, and Fully-Personalized Activity Classification Models

Pattern recognition methodologies, such as those utilizing machine learning (ML) approaches, have the potential to improve the accuracy and versatility of accelerometer-based assessments of physical activity (PA). Children with cerebral palsy (CP) exhibit significant heterogeneity in relation to imp...

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Bibliographic Details
Main Authors: Matthew N. Ahmadi, Margaret E. O’Neil, Emmah Baque, Roslyn N. Boyd, Stewart G. Trost
Format: Article
Language:English
Published: MDPI AG 2020-07-01
Series:Sensors
Subjects:
accelerometers
wearable sensors
exercise
measurement
GMFCS level
Online Access:https://www.mdpi.com/1424-8220/20/14/3976

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https://www.mdpi.com/1424-8220/20/14/3976

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